A geometry-aligned bi-fidelity surrogate maps low- and high-fidelity wildfire solutions to a common domain for improved reduced-basis reconstruction, lower error near fronts, and practical uncertainty quantification.
AK-MCS: An active learning reliability method combining kriging and monte carlo simulation.Structural Safety, 33 (2):145–154, 2011
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KDE-AIS trains a Gaussian process and kernel density surrogate from shared evaluations to build an adaptive importance sampling proposal that converges to the zero-variance optimum for efficient failure probability estimation.
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A geometry-aligned multi-fidelity framework for uncertainty quantification of wildfire spread
A geometry-aligned bi-fidelity surrogate maps low- and high-fidelity wildfire solutions to a common domain for improved reduced-basis reconstruction, lower error near fronts, and practical uncertainty quantification.
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Surrogate-Guided Adaptive Importance Sampling for Failure Probability Estimation
KDE-AIS trains a Gaussian process and kernel density surrogate from shared evaluations to build an adaptive importance sampling proposal that converges to the zero-variance optimum for efficient failure probability estimation.